This paper proposes a semi-nonparametric (SNP) methodology for computing portfolio value-at-risk (VaR) that is more accurate than both the traditional Gaussian-assumption-based methods implemented in the software packages used by risk analysts (RiskMetrics), and alternative heavy-tailed distributions that seem to be very rigid to incorporate jumps and asymmetries in the distribution tails (e.g. the Student’s t). The outperformance of the SNP distributions lies in the fact that Edgeworth and Gram-Charlier series represent a valid asymptotic approximation of any “regular” probability density function. In fact these expansions involve general and flexible parametric representations capable of featuring the salient empirical regularities of fin...
This paper analyses several volatility models by examining their ability to forecast the Value-at-Ri...
The market risk of a portfolio refers to the possibility of financial loss due to the joint movement...
This thesis comprises two essays that apply nonparametric methods to the estimation of portfolio all...
The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible an...
This article proposes a three-step procedure to estimate portfolio return distributions under the mu...
This paper sheds light on the evaluation of portfolio risk by assuming a distribution capable of inc...
The need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important lit...
textabstractAccurate prediction of the frequency of extreme events is of primary importance in many ...
textabstractIn this paper we examine the usefulness of multivariate semi-parametric GARCH models for...
We derive the statistical properties of the SNP densities of Gallant and Nychka (1987). We show that...
In this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating ...
textabstractWe propose a semi-parametric method for unconditional Value-at-Risk (VaR) evaluation. Th...
The accuracy of parametric, non-parametric and semi-parametric methods in predicting the one-day-ahe...
Currently, the financial institutions are exposed to different types of risks, which has increased t...
In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of...
This paper analyses several volatility models by examining their ability to forecast the Value-at-Ri...
The market risk of a portfolio refers to the possibility of financial loss due to the joint movement...
This thesis comprises two essays that apply nonparametric methods to the estimation of portfolio all...
The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible an...
This article proposes a three-step procedure to estimate portfolio return distributions under the mu...
This paper sheds light on the evaluation of portfolio risk by assuming a distribution capable of inc...
The need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important lit...
textabstractAccurate prediction of the frequency of extreme events is of primary importance in many ...
textabstractIn this paper we examine the usefulness of multivariate semi-parametric GARCH models for...
We derive the statistical properties of the SNP densities of Gallant and Nychka (1987). We show that...
In this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating ...
textabstractWe propose a semi-parametric method for unconditional Value-at-Risk (VaR) evaluation. Th...
The accuracy of parametric, non-parametric and semi-parametric methods in predicting the one-day-ahe...
Currently, the financial institutions are exposed to different types of risks, which has increased t...
In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of...
This paper analyses several volatility models by examining their ability to forecast the Value-at-Ri...
The market risk of a portfolio refers to the possibility of financial loss due to the joint movement...
This thesis comprises two essays that apply nonparametric methods to the estimation of portfolio all...